Quorum Sensing (QS) is a bacterial cell-to-cell communication mechanism allowing to share information about cell density, to adjust gene expression accordingly. Pathogens leverage QS to coordinate virulence and antimicrobial resistance, leading to distinctive population-level behaviors. To support rational design of synthetic biology strategies counteracting these mechanisms, we first mathematically model and compare two common QS architectures: one based on a single positive feedback loop to auto-induce signal molecule synthesis, the other including an additional positive feedback to increase signal molecule receptors production. Our comprehensive analysis of these QS structures and their equilibria highlights the differences in their bistable and hysteretic behaviors. An extensive sensitivity analysis is then performed, highlighting how parameter variations may lead to phenotype alterations in system behavior. Finally, building on our sensitivity analysis, we mathematically model four distinct QS inhibition strategies - signal molecule degradation, pharmaceutical inhibition, CRISPRi, and RNAi - which lead to the design of Quorum-Quenching (QQ) therapeutic approaches. Despite the underlying complex mechanisms, we demonstrate that the effect of the proposed QQ strategies can be captured by varying specific parameters within the QS models. We numerically analyze how these strategies affect the steady-state behavior of both QS models, identifying critical parameter thresholds for effective QS suppression.
{"title":"Quorum Sensing Model Structures Inspire the Design of Quorum Quenching Strategies","authors":"Chiara Cimolato;Gianluca Selvaggio;Luca Marchetti;Giulia Giordano;Luca Schenato;Massimo Bellato","doi":"10.1109/TMBMC.2025.3554671","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3554671","url":null,"abstract":"Quorum Sensing (QS) is a bacterial cell-to-cell communication mechanism allowing to share information about cell density, to adjust gene expression accordingly. Pathogens leverage QS to coordinate virulence and antimicrobial resistance, leading to distinctive population-level behaviors. To support rational design of synthetic biology strategies counteracting these mechanisms, we first mathematically model and compare two common QS architectures: one based on a single positive feedback loop to auto-induce signal molecule synthesis, the other including an additional positive feedback to increase signal molecule receptors production. Our comprehensive analysis of these QS structures and their equilibria highlights the differences in their bistable and hysteretic behaviors. An extensive sensitivity analysis is then performed, highlighting how parameter variations may lead to phenotype alterations in system behavior. Finally, building on our sensitivity analysis, we mathematically model four distinct QS inhibition strategies - signal molecule degradation, pharmaceutical inhibition, CRISPRi, and RNAi - which lead to the design of Quorum-Quenching (QQ) therapeutic approaches. Despite the underlying complex mechanisms, we demonstrate that the effect of the proposed QQ strategies can be captured by varying specific parameters within the QS models. We numerically analyze how these strategies affect the steady-state behavior of both QS models, identifying critical parameter thresholds for effective QS suppression.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"201-217"},"PeriodicalIF":2.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938711","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-25DOI: 10.1109/TMBMC.2025.3554674
Aiman Khalil;Kurt J. A. Pumares;Anne Skogberg;Pasi Kallio;Deirdre Kilbane;Daniel P. Martins
Molecular communication (MC) is an emerging framework enabling communication among biological cells and bio-nanomachines at nano and micro scales through biochemical molecules. Recent studies have identified exosomal transfer RNA-derived small RNAs (tsRNAs) as potential biomarkers for epilepsy. Consequently, researchers are exploring innovative methods to predict epileptic seizures through tsRNA measurements, using implantable micro/nanoscale biosensors. This paper presents a propagation model for biomarkers in a heterogeneous fluidic environment, composed of the brain extracellular space (ECS), a polyethersulfone (PES) hollow fiber tube, and a hydrogel (e.g., collagen) containing bioengineered sensing cells for biomarker detection. Our proposed model aims to support the design of biosensing devices for epileptic seizure prediction by characterizing the propagation of biomarkers released from neuronal cells in the brain ECS to the implant. We analyse the communication performance of the proposed system by evaluating propagation loss under varying conditions-brain ECS tortuosity, fiber membrane thickness, permeability, and bioengineered sensing cell density. Furthermore, we develop an MC link budget to assess communication between exosomal tsRNA biomarkers and bioengineered sensing cells, based on received biomarkers. We observed an approximate 8-fold loss in received signal strength, highlighting the impact of MC communication media physicochemical characteristics for accurately designing devices to predict epileptic seizures.
{"title":"Molecular Communications Loss Budget for tsRNA Detection in the Brain","authors":"Aiman Khalil;Kurt J. A. Pumares;Anne Skogberg;Pasi Kallio;Deirdre Kilbane;Daniel P. Martins","doi":"10.1109/TMBMC.2025.3554674","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3554674","url":null,"abstract":"Molecular communication (MC) is an emerging framework enabling communication among biological cells and bio-nanomachines at nano and micro scales through biochemical molecules. Recent studies have identified exosomal transfer RNA-derived small RNAs (tsRNAs) as potential biomarkers for epilepsy. Consequently, researchers are exploring innovative methods to predict epileptic seizures through tsRNA measurements, using implantable micro/nanoscale biosensors. This paper presents a propagation model for biomarkers in a heterogeneous fluidic environment, composed of the brain extracellular space (ECS), a polyethersulfone (PES) hollow fiber tube, and a hydrogel (e.g., collagen) containing bioengineered sensing cells for biomarker detection. Our proposed model aims to support the design of biosensing devices for epileptic seizure prediction by characterizing the propagation of biomarkers released from neuronal cells in the brain ECS to the implant. We analyse the communication performance of the proposed system by evaluating propagation loss under varying conditions-brain ECS tortuosity, fiber membrane thickness, permeability, and bioengineered sensing cell density. Furthermore, we develop an MC link budget to assess communication between exosomal tsRNA biomarkers and bioengineered sensing cells, based on received biomarkers. We observed an approximate 8-fold loss in received signal strength, highlighting the impact of MC communication media physicochemical characteristics for accurately designing devices to predict epileptic seizures.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 3","pages":"405-417"},"PeriodicalIF":2.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-19DOI: 10.1109/TMBMC.2025.3552959
Nayereh FallahBagheri;Özgür B. Akan
Molecular communication (MC) within the synaptic cleft is vital for neurotransmitter diffusion, a process critical to cognitive functions. In Alzheimer’s Disease (AD), beta-amyloid oligomers (A$beta $ os) disrupt this communication, leading to synaptic dysfunction. This paper investigates the molecular interactions between glutamate, a key neurotransmitter, and A$beta $ os within the synaptic cleft, aiming to elucidate the underlying mechanisms of this disruption. Through stochastic modeling, we simulate the dynamics of A$beta $ os and their impact on glutamate diffusion. The findings, validated by comparing simulated results with existing experimental data, demonstrate that A$beta $ os serve as physical obstacles, hindering glutamate movement and increasing collision frequency. This impairment of synaptic transmission and long-term potentiation (LTP) by binding to receptors on the postsynaptic membrane is further validated against known molecular interaction behaviors observed in similar neurodegenerative contexts. The study also explores potential therapeutic strategies to mitigate these disruptions. By enhancing our understanding of these molecular interactions, this research contributes to the development of more effective treatments for AD, with the ultimate goal of alleviating synaptic impairments associated with the disease.
{"title":"A Molecular Communication Perspective of Alzheimer’s Disease: Impact of Amyloid Beta Oligomers on Glutamate Diffusion in the Synaptic Cleft","authors":"Nayereh FallahBagheri;Özgür B. Akan","doi":"10.1109/TMBMC.2025.3552959","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3552959","url":null,"abstract":"Molecular communication (MC) within the synaptic cleft is vital for neurotransmitter diffusion, a process critical to cognitive functions. In Alzheimer’s Disease (AD), beta-amyloid oligomers (A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os) disrupt this communication, leading to synaptic dysfunction. This paper investigates the molecular interactions between glutamate, a key neurotransmitter, and A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os within the synaptic cleft, aiming to elucidate the underlying mechanisms of this disruption. Through stochastic modeling, we simulate the dynamics of A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os and their impact on glutamate diffusion. The findings, validated by comparing simulated results with existing experimental data, demonstrate that A<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>os serve as physical obstacles, hindering glutamate movement and increasing collision frequency. This impairment of synaptic transmission and long-term potentiation (LTP) by binding to receptors on the postsynaptic membrane is further validated against known molecular interaction behaviors observed in similar neurodegenerative contexts. The study also explores potential therapeutic strategies to mitigate these disruptions. By enhancing our understanding of these molecular interactions, this research contributes to the development of more effective treatments for AD, with the ultimate goal of alleviating synaptic impairments associated with the disease.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"186-200"},"PeriodicalIF":2.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TMBMC.2025.3525995
{"title":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","authors":"","doi":"10.1109/TMBMC.2025.3525995","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3525995","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"C2-C2"},"PeriodicalIF":2.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TMBMC.2025.3526017
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/TMBMC.2025.3526017","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3526017","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 1","pages":"C3-C3"},"PeriodicalIF":2.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-11DOI: 10.1109/TMBMC.2025.3550323
Mohammad Zoofaghari;Krizia Sagini;Martin Damrath;Azar Zargarnia;Håkon Flaten;Mladen Veletić;Alicia Llorente;Ilangko Balasingham
Extracellular vesicles (EVs) are lipid bilayer enclosed nanovesicles involved in intercellular communication. EVs are emerging as potential cancer biomarkers, providing insights into the condition of parent cancer cells. Their composition and entry into the bloodstream are influenced by factors such as tumor grade, type, and the configuration of the vascular network at the release site. In this work, we propose a computer simulation model to emulate the penetration of EVs into the bloodstream. We take into account convective and diffusive parameters that are influenced by the tumor’s characteristics, and the configuration of the vasculature and lymphatic network. We investigate the penetration rate of EVs into the bloodstream in terms of various parameters such as vessel wall permeability and the configuration of the vasculature and lymphatic networks. Our parametric study using a 2D model demonstrates that increasing the permeability coefficient, as observed in tumor tissue, could lead to a two-fold increase in EV penetration rate into the bloodstream. We believe that this model offers pre-experimental insights concerning liquid biopsy assays and the metastatic progression of the disease.
{"title":"In Silico Study of Bloodstream Penetrating Extracellular Vesicles","authors":"Mohammad Zoofaghari;Krizia Sagini;Martin Damrath;Azar Zargarnia;Håkon Flaten;Mladen Veletić;Alicia Llorente;Ilangko Balasingham","doi":"10.1109/TMBMC.2025.3550323","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3550323","url":null,"abstract":"Extracellular vesicles (EVs) are lipid bilayer enclosed nanovesicles involved in intercellular communication. EVs are emerging as potential cancer biomarkers, providing insights into the condition of parent cancer cells. Their composition and entry into the bloodstream are influenced by factors such as tumor grade, type, and the configuration of the vascular network at the release site. In this work, we propose a computer simulation model to emulate the penetration of EVs into the bloodstream. We take into account convective and diffusive parameters that are influenced by the tumor’s characteristics, and the configuration of the vasculature and lymphatic network. We investigate the penetration rate of EVs into the bloodstream in terms of various parameters such as vessel wall permeability and the configuration of the vasculature and lymphatic networks. Our parametric study using a 2D model demonstrates that increasing the permeability coefficient, as observed in tumor tissue, could lead to a two-fold increase in EV penetration rate into the bloodstream. We believe that this model offers pre-experimental insights concerning liquid biopsy assays and the metastatic progression of the disease.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"166-175"},"PeriodicalIF":2.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1109/TMBMC.2025.3547892
Muralikrishnna G. Sethuraman;Megan A. McSweeney;Mark P. Styczynski;Faramarz Fekri
Monitoring the levels of biomarkers for diagnostic applications has significant potential for impacts on patient care, but the measurement of all relevant biomarkers for a given set of conditions is often too expensive or unwieldy to be feasible at scale. Here, we propose a novel computational method for detecting changes in the levels of multiple target molecules from a complex sample via a small, cost-effective group of biosensors. We use the framework of density evolution (DE), a technique commonly used in the design of linear error-correcting codes for transmission over noisy channels, to develop an approach for localizing changes to a small subset of input signals based on a few simple output signals. As a biologically relevant testbed, we sought to detect the changes in the levels of multiple different microRNAs (miRNAs), which are nucleic acid molecules that are being increasingly studied and used as biomarkers. We accomplished this via the use of a class of molecules called “toehold switches” to create biosensors each capable of detecting multiple different miRNA sequences via a single output, with an overlap in sensitivity patterns between the different biosensors. A small number of these sensors were then used for inference of miRNA profiles. We demonstrate the potential utility of our approach with real data. Experimental results indicate the promising outcomes regarding the effectiveness of our method in detecting changes in miRNA concentrations.
{"title":"Construction of an Array of Biosensors Using Density Evolution for MicroRNA Monitoring","authors":"Muralikrishnna G. Sethuraman;Megan A. McSweeney;Mark P. Styczynski;Faramarz Fekri","doi":"10.1109/TMBMC.2025.3547892","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3547892","url":null,"abstract":"Monitoring the levels of biomarkers for diagnostic applications has significant potential for impacts on patient care, but the measurement of all relevant biomarkers for a given set of conditions is often too expensive or unwieldy to be feasible at scale. Here, we propose a novel computational method for detecting changes in the levels of multiple target molecules from a complex sample via a small, cost-effective group of biosensors. We use the framework of density evolution (DE), a technique commonly used in the design of linear error-correcting codes for transmission over noisy channels, to develop an approach for localizing changes to a small subset of input signals based on a few simple output signals. As a biologically relevant testbed, we sought to detect the changes in the levels of multiple different microRNAs (miRNAs), which are nucleic acid molecules that are being increasingly studied and used as biomarkers. We accomplished this via the use of a class of molecules called “toehold switches” to create biosensors each capable of detecting multiple different miRNA sequences via a single output, with an overlap in sensitivity patterns between the different biosensors. A small number of these sensors were then used for inference of miRNA profiles. We demonstrate the potential utility of our approach with real data. Experimental results indicate the promising outcomes regarding the effectiveness of our method in detecting changes in miRNA concentrations.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 3","pages":"335-343"},"PeriodicalIF":2.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1109/TMBMC.2025.3546503
Zhen Cheng;Heng Liu;Ziyan Xu;Jiaxin Li;Kaikai Chi
Diffusive molecular communication (DMC) utilizes the emission, diffusion and reception of molecules to transmit information. It has promising prospects in the field of drug delivery. The estimation of emission time and arrival time of molecules in DMC system plays important roles in the resource consumption at the receivers. Existing traditional strategies for the derivation of emission time and arrival time mainly focus on known channel state information (CSI). In this paper, we propose a deep learning method for estimating emission time and arrival time of the molecules in DMC system with unknown CSI by using Transformer-based model, respectively. The simulation results show that the emission time and arrival time of molecules can be accurately estimated by the Transformer-based model which exhibits better estimation and generalization abilities than deep neural network (DNN) model.
{"title":"Deep Learning-Based Estimation of Emission Time and Arrival Time in Diffusive Multi-Receiver Molecular Communication","authors":"Zhen Cheng;Heng Liu;Ziyan Xu;Jiaxin Li;Kaikai Chi","doi":"10.1109/TMBMC.2025.3546503","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3546503","url":null,"abstract":"Diffusive molecular communication (DMC) utilizes the emission, diffusion and reception of molecules to transmit information. It has promising prospects in the field of drug delivery. The estimation of emission time and arrival time of molecules in DMC system plays important roles in the resource consumption at the receivers. Existing traditional strategies for the derivation of emission time and arrival time mainly focus on known channel state information (CSI). In this paper, we propose a deep learning method for estimating emission time and arrival time of the molecules in DMC system with unknown CSI by using Transformer-based model, respectively. The simulation results show that the emission time and arrival time of molecules can be accurately estimated by the Transformer-based model which exhibits better estimation and generalization abilities than deep neural network (DNN) model.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"257-268"},"PeriodicalIF":2.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Molecular communication (MC) employs chemical molecules for information transfer in environments where electromagnetic signals are ineffective. However, the diffusion mechanism introduces signal-dependent noise (SDN), complicating accurate signal recovery. Traditional model-based methods struggle to handle SDN’s complex dynamics and depend heavily on optimal parameter tuning, limiting their adaptability to temporal variations. To tackle these challenges, this paper introduces a hybrid recurrent neural network (RNN) model that effectively captures both short- and long-term dependencies within MC signals, surpassing the performance of single RNN models and traditional approaches. This model offers a promising data-driven solution for noise mitigation in MC, with its effectiveness validated through numerical simulation results.
{"title":"Hybrid Recurrent Neural Network for Signal-Dependent Noise Suppression in Molecular Communication","authors":"Cheng Xiang;Yaqing Zhang;Yu Huang;Weiqiang Tan;Xuan Chen;Miaowen Wen","doi":"10.1109/TMBMC.2025.3546208","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3546208","url":null,"abstract":"Molecular communication (MC) employs chemical molecules for information transfer in environments where electromagnetic signals are ineffective. However, the diffusion mechanism introduces signal-dependent noise (SDN), complicating accurate signal recovery. Traditional model-based methods struggle to handle SDN’s complex dynamics and depend heavily on optimal parameter tuning, limiting their adaptability to temporal variations. To tackle these challenges, this paper introduces a hybrid recurrent neural network (RNN) model that effectively captures both short- and long-term dependencies within MC signals, surpassing the performance of single RNN models and traditional approaches. This model offers a promising data-driven solution for noise mitigation in MC, with its effectiveness validated through numerical simulation results.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"283-291"},"PeriodicalIF":2.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1109/TMBMC.2025.3546207
Luyao Zhang;Yue Sun;Dong Du;Yifan Chen
This study proposes a novel light-driven nanorobots swarm (NS) aggregation method to enhance tumor targeting efficiency. To replicate the structured and directional flow of density blood vessels near tumors, we employed a Manhattan-geometry vasculature (MGV) model, which mimics the complex, density-connected vasculature near the tumor site. This model significantly influences NS navigation and aggregation behavior, providing more realistic movement dynamics insights. We analyzed NS dynamics under light illumination, focusing on drag and thermophoretic forces. Comparisons with magnetic field-driven and non-external force strategies across three objective functions show that light-driven targeting increases efficiency by 4% to 46% and reduces targeting time by up to 27.9%. The MGV model enables precise predictions of NS movement, optimizing aggregation toward tumor tissues. These findings demonstrate the potential of light-driven NS aggregation to enhance tumor-targeting therapies, offering advantages over magnetic control in complex biological environments, with implications for photothermal therapy and precision drug delivery.
{"title":"Dynamics and Kinetics of Light-Driven Nanorobots Swarm Aggregation for Tumor Targeting","authors":"Luyao Zhang;Yue Sun;Dong Du;Yifan Chen","doi":"10.1109/TMBMC.2025.3546207","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3546207","url":null,"abstract":"This study proposes a novel light-driven nanorobots swarm (NS) aggregation method to enhance tumor targeting efficiency. To replicate the structured and directional flow of density blood vessels near tumors, we employed a Manhattan-geometry vasculature (MGV) model, which mimics the complex, density-connected vasculature near the tumor site. This model significantly influences NS navigation and aggregation behavior, providing more realistic movement dynamics insights. We analyzed NS dynamics under light illumination, focusing on drag and thermophoretic forces. Comparisons with magnetic field-driven and non-external force strategies across three objective functions show that light-driven targeting increases efficiency by 4% to 46% and reduces targeting time by up to 27.9%. The MGV model enables precise predictions of NS movement, optimizing aggregation toward tumor tissues. These findings demonstrate the potential of light-driven NS aggregation to enhance tumor-targeting therapies, offering advantages over magnetic control in complex biological environments, with implications for photothermal therapy and precision drug delivery.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 2","pages":"269-282"},"PeriodicalIF":2.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：